Who Gets to Be “Canadian”? How Race Has Operated in Keeping Canada White
Bibliographic record
Abstract
“ Where are you from?” is often a question that is asked of some Canadians – typically, Canadians for whom skin colour, race, accent, language (other than English and French), name (last, first and nick-name), religion, place of residence, “appearance,” and the organizations or clubs to which they belong, play a role in why they are being asked the question in the first place. But why are “ assumed non-accented ‘non-visible’ Canadians not asked this question? And when individuals – based on identities by which they are read as “not from Canada” – answer the question with “Canadian,” why would they get a follow-up question: “Where are your parents from?”;and/ or “Where are you really from?” In taking up this question, I discuss the treatment, settlement and experiences of Black/African, Chinese, and South Asian Canadians in terms of how Canadian laws, policies, and practices have operated to keep them from entering the country, and in turn shaped their life conditions once here. With reference to how, building on colonialism, systemic racism structures the conditions racialized people encounter daily, I go on to discuss the ways in which the history of populating Canada accounts for racialized peoples' experiences with anti-Indigenous, anti-Black, and anti-Asian racisms in living on Turtle Island.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".